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international conference on Communication Technology (ICCT)

作者： Fang Deng Xingrong Huang Shujun Sun Shengliang Peng College of Information Science and Engineering Huaqiao University Xiamen China

The tasks of identifying which modulation format and channel coding scheme have been utilized by the wireless signal are crucial to intelligent communications, electronic warfare, and spectrum management. Currently, most research on these tasks digs into either modulation identification or channel coding identification, while the joint identification of modulation and channel coding has not been fully investigated. In this paper, we propose two joint identification algorithms based on deep learning to handle the tasks of modulation identification and channel coding identification simultaneously. The first algorithm adopts a successive architecture, in which two neural networks are used to handle the two identification tasks, respectively. The second algorithm exploits multi-task deep learning, with which two identification tasks can be completed using a single neural network. Experiments results show that, considering the candidate set of three modulation formats and two channel coding schemes, both algorithms achieve satisfactory identification accuracy, and the latter is superior to the former with the accuracy improvement of 10%.

关键词： Deep learning Wireless communication Electronic warfare Neural networks Modulation Multitasking Channel coding

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Non-invasive software architecture for data pipelines with legacy support in smart manufacturing

Non-invasive software architecture for data pipelines with l...

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Working IEEE/IFIP conference on Software Architecture

作者： Alberto Ceselli Giuseppe De Martino Patrizia Scandurra Università Degli Studi di Milano Milano Italy Università Degli Studi di Milano Par-Tec S.p.A. Milano Italy Università Degli Studi di Bergamo Bergamo Italy

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

Smart manufacturing relies on the digitization of all the industrial processes, from production to business operations. It uses Industrial Internet of Things (IIoT) principles to equip devices with smart sensors and actuators, integrating machines and software through data collection, advanced computational methods, and remote control. Our research is motivated by a real digital transition application in the luxury fashion in Italy. The customers wish to update legacy systems, to comply with new Industry 4.0 standards. Due to industrial property requirements, as well as brand secrets, they require the whole architecture to run on-premises. A further requirement is that the system installation must be non-invasive, potentially running on systems with frugal setups in terms of hardware and *** to such requirements and principles, this paper proposes an architecture for data pipeline in smart manufacturing that runs on-premises, offering support to legacy machines. It is capable of identifying unknown hardware, in terms of semantics of its sensors. The core component of such a concrete architecture is an innovative Extract-Transform-Load (ETL) connector, called sEmantic eXtended ETL (exETL), that manages numerous heterogeneous data sources, and recognizes and configures automatically new machinery sensors. It employs a dedicated Machine learning (ML) pipeline. The flexibility of the proposed architecture is compared to alternative solutions that exploit existing technologies. Its computational effectiveness is assessed by building an emulated environment, and running extensive experiments on real data. Our results show that our data pipeline is lightweight, more flexible than competitors, and capable of integrating legacy or new machinery seamlessly.

关键词： Software architecture Pipelines Semantics Computer architecture Hardware Fourth Industrial Revolution Machinery intelligent sensors Smart manufacturing Industrial Internet of Things

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Es-DNLC: A Deep Neural Network Control with Exponentially Stabilizing Control Lyapunov Functions for Attitude Stabilization of PAV 22

Es-DNLC: A Deep Neural Network Control with Exponentially St...

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22nd international conference on Control, Automation and Systems, ICCAS 2022

作者： Jang, Minseok Hyun, Jeongseok Kwag, Taeho Gwak, Chan Jeong, Chanyoung Nguyen, Tuan Anh Lee, Jae-Woo Konkuk University Department of Aerospace Information Engineering Seoul Korea Republic of Konkuk University Konkuk Aerospace Design-Airworthiness Institute Seoul Korea Republic of

ISBN: (纸本)9788993215243

Attitude stabilization is of paramount importance in the flight control of personal aerial vehicle (PAV) in the future urban air mobility (UAM). This study proposes to adopt a deep neural network (DNN) with exponentially stabilizing control Lyapunov functions (es-CLF) as a control framework (called, es-DNLC) for the stabilization of a KP-1 eVTOL PAV in multi-copter mode. The es-DNLC uses exponentially stabilizing control Lyapunov Function(es-CLF) as a learning policy in the DNN training to guarantee the robustness against disturbances. The robustness is enhanced and verified by an area increase of region of attraction (ROA) after adopting the trained DNN into the KP-1 control system. We implemented the proposed control framework in an open source autopilot system (PX4) along with software in the loop (SITL) in Gazebo simulator in which a wind gust is injected as a sudden disturbance in the simulation. A wind tunnel test was performed to increase the accuracy of the Gazebo simulation by utilizing high-fidelity propulsion data of the KP-1's motors. The effectiveness of the adopted control framework is compared with linear quadratic regulator (LQR) which is also the initial control of es-DNLC before training. The finding of this study shows that es-DNLC compared to LQR can guarantee a higher level of robustness of the system against disturbances and aerodynamic uncertainties. © 2022 ICROS.

关键词： Robust control

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GCN-Enhanced Multi-Agent MAC Protocol for Vehicular Communications

GCN-Enhanced Multi-Agent MAC Protocol for Vehicular Communic...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Mengfei Li Wei Wang Shijie Feng Tiange Fu Yan Wang Hui Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Sichuan Jiuzhou Electronic Group Co. Ltd. Mianyang China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In the vehicular ad-hoc network (VANET) environment, the constant changes in traffic systems due to the real-time mobility of vehicles pose a challenge in developing communication strategies that can swiftly adapt to dynamic topological variations. Traditional protocols such as CSMA/CD offer dynamic access to shared media, but their efficiency is compromised by CSMA/CA's collision avoidance strategy, which reduces channel use due to its backoff algorithm. This paper introduces a novel Graph Convolutional Network-based Multi-Agent Reinforcement learning MAC protocol (GM-MAC) for vehicular networks. Unlike the Neuro-DCF protocol, GMMAC incorporates local vehicular data directly, enabling more context-aware learning. Compared to other GNN-based MARL approaches like GraphComm, GMMAC better handles real-time changes and interactions among vehicles. Experimental results show that GMMAC significantly improves successful transmission numbers and reduces collisions, outperforming traditional CSMA/CA methods and other MARL approaches without GCN integration.

关键词： Protocols Heuristic algorithms Scalability Refining Vehicular ad hoc networks Reinforcement learning Media Real-time systems Vehicle dynamics Observability

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How to easily make a policy network of reinforcement learning robust without physical modeling

How to easily make a policy network of reinforcement learnin...

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international conference on Control, Automation and Systems ( ICCAS)

作者： HyeonBeen Park SeungMin Baek SooHee Han Department of Convergence IT Engineering Pohang University of Science and Technology Pohang South Korea

In this paper, we propose ensemble inverse model network based disturbance observer (EIMN-DOB) to improve the robustness of the policy network (PN) which is a training result of policy based reinforcement learning (RL), without physical modeling. EIMN-DOB uses the ensemble model of the inverse model network (IMN), which acts as a nominal inverse model, and can estimate and cancel model uncertainty and disturbance like a typical disturbance observer (DOB) without a physical modeling. Because EIMN is trained from the data used in training RL, the additional training data for expressing the inverse model are not required. The experiments in this paper appeared that the PN of soft actor critic(SAC) combined with EIMN-DOB maintains control performance even in the presence of disturbance in continuous control benchmark tasks based on Mujoco physics engine. When the trained PN is used with EIMN-DOB in the real environment, the control performance in simulator can be preserved in the real environment, and it is expected to be utilized to minimize the sim-to-real gap of RL.

关键词： Training Uncertainty Training data Reinforcement learning Benchmark testing Disturbance observers Robustness

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Vision-based Reinforcement learning: Moving object grasping with a Single Active-view Camera

Vision-based Reinforcement learning: Moving object grasping ...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Seongwon Jang Hyemi Jeong Hyunseok Yang Department of Mechanical Engineering Yonsei University Seoul Korea

Traditional methods of grasping moving objects use visual information from camera that is statically mounted overlooking the task environment. However, when the camera view is hindered by an obstacle or a robot itself performing tasks, these methods cannot produce good results for grasping moving objects. This paper proposes a new moving object grasping method using only single active RGB camera attached to the robot with utilization of model free deep reinforcement learning strategy with Soft Actor-Critic(SAC) algorithm. Our proposed system uses RGB image data to train our proposed system and to further improve training and success rate, we integrate vision data with robot’s kinematic state information during training. A dense reward function is designed for efficient learning of the agent and training is done on simulation using a Ufactory xArm 7 manipulator. Experimental results demonstrate that with our proposed method, the manipulator can learn to autonomously grasp the moving object.

关键词： Training Visualization Robot vision systems Force Grasping Reinforcement learning Kinematics

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Secure e-Voting System—A Review 22nd

Secure e-Voting System—A Review

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Devi, Urmila Bansal, Shweta Computer Science and Engineering Department K.R. Mangalam University Haryana Gurugram India

ISBN: (纸本)9783031274084

Security is a requirement which is needed in all aspects of life. In this computer world when everything is online then online threats are common to all. e-Voting system is now the need of the hour. As in this internet era everyone wants everything just one click away including shopping, online food ordering and even finding life partners online then why not voting online. In traditional voting systems everyone has to go to cast their votes on polling booths but with few reasons only few go for voting. There are many reasons like—people do not want to stand in long queues, they are out of station on the day of voting etc. So to keep all these in mind online voting can solve such problems and make more vote casting to make the election successful. But there are many security measures that come into consideration for the online voting system implementation such as—Ballot stuffing threats, Denial-of-service attack, double spending problem, 51% attacks and many more. This paper will give a comprehensive survey about the possible threats in online voting system and different types of cryptographic techniques like—Asymmetric key cryptography, Elliptic-Curve Cryptography (ECC), Identity Based Encryption (IBE) cryptography etc. which can be used to prevent from such attacks and also provide a comparison summary based on the e-Voting security requirements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Voting machines

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A Minority Class Boosted Framework for Adaptive Access Control Decision-Making 22nd

A Minority Class Boosted Framework for Adaptive Access Contr...

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22nd international conference on Web Information Systems engineering (WISE)

作者： You, Mingshan Yin, Jiao Wang, Hua Cao, Jinli Miao, Yuan Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Australia

ISBN: (纸本)9783030908881;9783030908874

Access control is an effective way to prevent data exfiltration from insiders. Recently, machine learning algorithms have been widely used in access control decision-making. However, these algorithms usually fail to consider the dynamic class imbalance in access control problems and thus achieve poor performance on minority classes. In addition, concept drift problems caused by evolving user and resource attributes, user behaviours and access environments are also challenges to tackle. This paper proposes a minority class boosted framework for adaptive access control methods. Specifically, this framework uses a continuous incremental batch learning strategy instead of a batch learning approach to handle the concept drift problem adaptively. Furthermore, a boosting window (BW) algorithm within the framework is specially designed to boost the performance of the minority class, thus, to decrease false positive decisions. The proposed framework is evaluated on a well-known Amazon employee access dataset and results demonstrate the effectiveness and flexibility of the proposed framework and BW algorithm.

关键词： Access control Online learning data imbalance Boosting window

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BlackFeather: A framework for background noise forensics

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FORENSIC SCIENCE international-DIGITAL INVESTIGATION 2022年 42卷

作者： Li, Qi Sovernigo, Giuliano Lin, Xiaodong Univ Guelph Sch Comp Sci 50 Stone Rd East Guelph ON Canada

Historically, criminal investigations hinging on recorded audio data required manual application of forensic techniques to extract relevant information. These methods usually focus mainly on voices and speaker identification, but rarely focus on the wealth of forensic information available in the background noises present in the recording. Our paper introduces methods of automatically extracting, separating, and classifying background noises, allowing for the difficult, time-consuming process of audio analysis to be handled by software. Once the audio has been classified and examined by our proposed tools, the results can be used by investigators and forensic experts to aid in traditional investigative methods. Using environment information as an example, we propose a fully automated environment inference process based on background noise. Detailed experimental results show that our framework is effective and fast. Our proposed framework intends to provide a neat, automated, and accurate analysis of the information present in background audio, and to provide a new source of forensic information for investigators to leverage. In contrast to existing similar work, our scheme not only realistically considers mixed human voice speech, but also considers the case of multiple background noise mixes. To the best of our knowledge, this is the first forensic work that considers background noise in a complex environment. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Background noise Forensics Deep learning Environment

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EEG Signal Processing for Action Recognition Using Machine learning Paradigms 22

EEG Signal Processing for Action Recognition Using Machine L...

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22nd OITS international conference on Information Technology, OCIT 2024

作者： Abirami, S.P. Misha Chandar, B. Karthikeyan, G. Elakkiya, E. Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering VIT-AP University Andhra Pradesh India Department of Computer Science and Engineering SRM UniversityAP Andhra Pradesh India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Many intriguing applications, such as the ability to move prosthetic limbs and enable more fluid man-machine contact, may be made possible by automatic interpretation of brain readings. The problem of precisely categorizing EEG signals linked with memory categories is tackled first in the inquiry. With the restricted availability of pre-trained models for such signal classification, a Convolution-based Neural Network (CNN) is constructed from scratch. By using EEG recordings from UC Berkeley's Bio-Sense Lab, this study seeks to improve memory recall using machine learning and precise feature selection. Fifteen participants' EEG data are converted into the frequency domain, and the amplitudes are used as key characteristics. The selection of these qualities is improved by a self-attention mechanism, which maximizes the distinction among various memory categories. The primary focus is to evaluate the performance of the most advanced algorithms, with the secondary objective of outperforming previous methods in terms of classification accuracy. A fine-tuned subset of the frequency-based characteristics is evaluated using a Support Vector Machine (SVM) classifier. By showcasing the efficiency of self-attention in honing feature subsets, this study highlights the significance of feature engineering in EEG-based memory classification. This method is positioned as a promising advancement in the analysis of EEG data since it improves the separation between memory categories through the application of frequency-domain modifications and SVM classifiers. Furthermore, investigating time series features shows how well they may capture intricate patterns, pointing to fresh avenues for future neuro-informatics and cognitive study. © 2024 IEEE.

关键词： Bio sense Lab Convolution-based Neural Network (CNN) EEG signal classification memory categorization neuroscientific research time series features

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